support Ngspice and Xyce circuit simulatorssupport Linux, Windows and Mac OS X platforms
licensed under GPLv3 therms
implement an Ngspice shared library binding using CFFI which support external sources
implement (partial) SPICE netlist parser
implement an Oriented Object API to define circuit
export simulation output to Numpy arrays
plot using Matplotlib
handle units
work with Kicad schematic editor
implement a documentation generator
provides many examples
How to install it ?Look at the installation section in the documentation.
Circuit Simulation In Python
This project is definitely worth a look if you have any interest in signal processing (digital or analog) or even if you have never heard of SPICE before and want an easier way of simulating a circuit before prototyping one on a breadboard.
We will build a half adder circuit with 2 inputs and 2 outputs. See figure 4.The Sum output is the same as the XOR gate above. That is the sum bit is one ifone and only one of the input bits is 1. The carry out is 1 only if both inputsare 1. For that we need an AND gate.
We now generalize the above to quantum circuits.Instead of bits, we have qubits.A qubit can be 0, 1, or a superposition of 0 and 1.So if you write out its vector, it can have numbers in different indices.E.g.,
This covers almost all the requirements for simulating the action of quantum circuit of your choosing, assuming you have the computational power to do so! So long as you can reduce the circuit you are interested in into an appropriate set of 1- and 2-qubit gates. Doing this in the generic case is still a bit of a research topic, but many algorithms can already be found in this form, so it should be reasonably easy to find a few to play with!
QuantumCircuit.draw() (called by circuit.draw() in the code) displays your circuit in one of the various styles used in textbooks and research articles. In the circuit obtained after visualization command , the qubits are ordered with qubit zero at the top and qubit one below it. The circuit is read from left to right, representing the passage of time.
The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.
Simply based on wikipedia english page of Chua's circuit. It seems that you failed to provide an expression of the function f describing the electrical response of the nonlinear resistor. So, starting from the given equations and expression of f, here's my try with odeint:
Learn how analog circuit simulators work with these easy to use numerical recipes implemented in the popular Python programming environment. This book covers the fundamental aspects of common simulation analysis techniques and algorithms used in professional simulators today in a pedagogical way through simple examples. The book covers not just linear analyses but also nonlinear ones like steady state simulations. It is rich with examples and exercises and many figures to help illustrate the points. For the interested reader, the fundamental mathematical theorems governing the simulation implementations are covered in the appendices.
Mikael Sahrling is a Principal Electronics Engineer working on developing high speed electrical interfaces for the Test & Measurement and communication industries. He has about 25 years of experience developing integrated circuits and many of the projects he has been a part of has brought in many millions of dollars in revenue. He has worked for many of the leading analog chip companies like Semtech Corporation, Maxim Integrated, Tektronix Inc. and IPG Photonics where he is the lead Analog Design Engineer. His special interest is in high speed active and passive design with a bandwidth of several tens of GigaHertz.
Unlike most other circuit analysis programs, Lcapy stores all values and expressions symbolically. It utilizes the powerful Python symbolic mathematics package, Sympy (Joyner et al., 2012; Meurer et al., 2017). The primary advantage of symbolic analysis is that it provides an exact solution and avoids the trial-and-error nature of system analysis using numerical simulation. Moreover, it avoids the instability and inaccuracy associated with numerical integration required by time-stepping simulation. The symbolic solution provides insight into system behavior, for example, how a component influences the poles of a power-electronics controller (Heffernan, Mitchell & Hayes, 2020). Using symbolic mathematical tools allows expressions to be easily converted to standard forms, such a pole-zero-gain or partial fraction, to gain further insight into system behaviour.
Open-source tools are available for numerical circuit simulation, including SPICE (Nagel & Pederson, 1973) and its many derivatives, such as PySpice ( ), and Qucs (Brinson & Jahn, 2009), an enhancement to SPICE. These tools are excellent for the generation of numerical solutions to general, non-linear circuits. However, they do not provide the insight of symbolic analysis.
A number of symbolic circuit analysis packages have been developed, primarily through the 1980s and 1990s (Fernández & Rodríguez-Vázquez, 1996). These include: ISAAC (Interactive symbolic analysis of analog circuits), a common Lisp program for integrated circuit design that uses compacted modified nodal analysis (CMNA) with heuristics to reduce the number of symbolic term cancellations (Walscharts, Gielen & Sansen, 1989; Gielen, Walscharts & Sansen, 1989); ASAP (analog symbolic analysis program), a C program that expands transistors in SPICE netlist using library models for small-signal symbolic analysis (Fernández, Rodríguez-Vázquez & Huertas, 1990); Sspice, a C program for small-signal analysis that uses biasing information to simplify the transfer function (Wierzba et al., 1989; Srivastava, Wierzba & MacKay, 1990); AnalogSifter, designed for large integrated circuits using approximate transfer functions (Hsu & Sechen, 1993, 1994); SNAP (Symbolic and Numerical Analysis Program), a Windows program that linearises non-linear circuits around an operating point and can approximate the symbolic analysis (Biolek, 2000; Kolka, Biolek & Biolkova, 2008); SCIASCA, a Visual Java++/Maple program using nullor-based models (Tlelo-Cuautle et al., 2004); SCAM ( ) (symbolic circuit analysis in MATLAB); Akhab ( ), a Python program with a SPICE-like command sequence; Sapwin/SapecNG/QSapecNG, Windows-based C++ programs (Grasso et al., 2014); MSCAM (modified symbolic circuit analysis in MATLAB) (Ushie, Abbod & Ashigwuike, 2015); AICE (analog IC explorer), a C++ program with web-based interface that uses advanced graph techniques (Shi, 2017); CircuitNav ( ), a web-based application for generating a symbolic system of equations given a netlist, and SliCAP ( -electronics.eu/slicap/slicap.html) (Symbolic Linear Circuit Analysis Program), a Python (previously MATLAB) program for teaching circuit analysis.
Most of these tools are are designed specifically for integrated circuit analysis (Hsu & Sechen, 1993), are tied to a particular operating system, and are no longer available. A possible reason is that most are not open-source (Huelsman, 1996) and thus have not been maintained over time.
The author has used Lcapy for modelling low-noise operational amplifier circuits, designing analog and digital filters, simulating polyphase power systems, fitting models to electrode impedance data for electromagnetic flowmeters, designing controllers for power-electronics, modelling impedance compensation systems for biomedical instrumentation, and modelling piezoelectric transducers. He and others have also used Lcapy for producing textbook quality schematics, generating exam questions, checking exam and homework answers, and teaching students circuit theory.
A common circuit analysis problem is to find the transfer function between two pairs of nodes. There are several ways this can be achieved with Lcapy. For example, consider the filter network shown in Fig. 3. First, the independent sources need to be killed and an arbitrary Laplace-domain voltage source connected across the input nodes. The transfer function is then found from the ratio of the voltage measured across the output nodes to the input voltage. An alternative approach is to use the transfer() method which implicitly kills any independent sources. For example:
The state-space analyser can generate state-transition matrices, system transfer functions, system impulse responses, and the characteristic polynomial. For example, the characteristic polynomial for the circuit in Fig. 4 is generated by:
There are two special superposition classes: SuperpositionCurrent and SuperpositionVoltage. These represent signals as a superposition of DC, phasor, transient, and noise signals. They collate the results from the different circuit analysis methods before conversion to the time domain, Laplace domain, etc. 2ff7e9595c
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